Abstract:
Connectionist modeling offers a useful computational framework for exploring
the nature of normal and impaired cognitive processes. The current work
extends the relevance of connectionist modeling in neuropsychology to address
issues in cognitive rehabilitation: the degree and speed of recovery through
retraining, the extent to which improvement on treated items generalizes to
untreated items, and how treated items are selected to maximize this
generalization. A network previously used to model impairments in mapping
orthography to semantics is retrained after damage. The degree of relearning
and generalization varies considerably for different lesion locations, and has
interesting implications for understanding the nature and variability of
recovery in patients. In a second simulation, retraining on words whose
semantics are atypical of their category yields more generalization than
retraining on more typical words, suggesting a counterintuitive strategy for
selecting items in patient therapy to maximize recovery. In a final
simulation, changes in the pattern of errors produced by the network over the
course of recovery is used to constrain explanations of the nature of recovery
of analogous brain-damaged patients. Taken together, the findings demonstrate
that the nature of relearning in damaged connectionist networks can make
important contributions to a theory of rehabilitation in patients.

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